CSEM for CO2 Storage – Feasibility Study at Smeaheia to Optimise Acquisition

In this work, we evaluate the use of controlled-source electromagnetics (CSEM) for CO2 monitoring at Smeaheia, a possible candidate for future phases of the Norwegian full-scale CCS project. CSEM is sensitive to electrically resistive material replacing conductive pore water in the pore space, which enables to infer volumetric estimates of the injected CO2 in the formation. CSEM is often used in combination with high-resolution seismic reflection data due to the sensitivity of the two methods to complementary physical properties. Here, we present a technique to optimise the CSEM survey parameters for efficient 4D surveying. Realistic synthetic models prior to and after injection are derived from reservoir modelling and converted to electrical resistivities. Inversion tests are carried out in 2D for the baseline and monitor cases considering realistic data errors. We show that the resistivity changes due to CO2 injection can be monitored using CSEM. We discuss the optimal orientation of the receivers, frequency range and transmitter-receiver offset. We finally discuss a strategy for optimal survey design based on the sensitivity to the CO2 plume.publishedVersio

Apport de l'inversion des formes d'onde sismique - approximation 2D élastique - à la caractérisation des milieux de la proche surface (cavités souterraines, glissements de terrain)

PARIS-BIUSJ-Physique recherche (751052113) / SudocPARIS-BIUSJ-Sci.Terre recherche (751052114) / SudocSudocFranceF

Offset dependence of overburden time-shifts from ultrasonic data

Depletion or injection into a reservoir implies stress changes and strains in the reservoir and its surroundings. This may lead to measurable time-shifts for seismic waves propagating in the subsurface. To better understand the offset dependence of time-shifts in the overburden, we have systematically quantified the time-shifts of three different overburden shales in controlled laboratory tests. These experiments may be viewed as an analogue to the time-shifts recorded from seismic field surveys. For a range of different stress paths, i.e. the ratio between the horizontal and the vertical stress changes, the changes of the P-wave velocities in different directions were measured such that the offset dependence of time-shifts for different stress paths could be studied. The time-shifts are stress path dependent, which is particularly pronounced at large offsets. For all stress paths the time-shifts exhibit a linearly decreasing trend with increasing offset, i.e. a negative offset-gradient. At zero offset, for which the ray path is normal to the bedding, the time-shifts are similar for all investigated stress paths. The isotropic stress path is associated with the smallest offset-gradient of the time-shifts. Contrary, the constant-mean-stress path shows the largest gradient with a flip in the polarity of the time-shifts for the largest offsets. The separate contributions from the strain and velocity changes to the time-shift were also quantified. The time-shifts for the isotropic stress path are dominated by the contribution from velocity changes at all offsets. Contrary, the strain contributes significantly to the time-shifts at small offsets for the constant-mean-stress path. This shows that the offset dependence in pre-stack seismic data may be a key to understand the changes of subsurface stresses, pore pressure and strain upon depletion or injection. To utilize this knowledge from laboratory experiments, calibrated rock physics models and correlations are needed to constrain the seismic time-shifts and to obtain an adequately updated geological model reflecting the true anisotropic nature of the subsurface. This may have important implications for improved recovery and safety, particularly in mature fields.publishedVersio

CSEM for CO2 Storage – Feasibility Study at Smeaheia to Optimise Acquisition

In this work, we evaluate the use of controlled-source electromagnetics (CSEM) for CO2 monitoring at Smeaheia, a possible candidate for future phases of the Norwegian full-scale CCS project. CSEM is sensitive to electrically resistive material replacing conductive pore water in the pore space, which enables to infer volumetric estimates of the injected CO2 in the formation. CSEM is often used in combination with high-resolution seismic reflection data due to the sensitivity of the two methods to complementary physical properties. Here, we present a technique to optimise the CSEM survey parameters for efficient 4D surveying. Realistic synthetic models prior to and after injection are derived from reservoir modelling and converted to electrical resistivities. Inversion tests are carried out in 2D for the baseline and monitor cases considering realistic data errors. We show that the resistivity changes due to CO2 injection can be monitored using CSEM. We discuss the optimal orientation of the receivers, frequency range and transmitter-receiver offset. We finally discuss a strategy for optimal survey design based on the sensitivity to the CO2 plume

CO2 saturation estimates at Sleipner (North Sea) from seismic tomography and rock physics inversion

acceptedVersio

Bayesian rock-physics inversion: Application to CO2 storage monitoring

acceptedVersio

Artificial Intelligence for Well Integrity Monitoring Based on EM Data

Monitoring of integrity of plugged and abandoned (P&A'ed) wells is of interest for the oil and gas industry and for CO2 storage. The purpose of this study is to develop artificial intelligence (AI)-based approaches to detect anomalies or defects when monitoring permanently plugged wells. The studied solution is based on the analysis of electromagnetic (EM) data. We consider an offshore setting where the EM signal is generated in presence of a P&A'ed well and the resulting electric field is recorded at the seafloor. Numerical simulations are used to train an AI algorithm to classify the modelled EM features into predefined well integrity classes. We consider four scenarios: (1) no well, (2) well with three 20 meters thick cement barriers of thickness, (3) well with three cement barriers of 60 meters thickness, and (4) well with three cement barriers of 100 meters thickness. Convolutional neural networks (CNNs) are tested as the AI algorithm in this study. After training the algorithm on 80% of the data, it shows an accuracy of 95.36% on the test data. P&A'ed well integrity monitoring currently remains limited to local observation and symptom identification, but this study shows that there is great potential for developing remote non-invasive well integrity monitoring techniques

Bayesian rock-physics inversion: Application to CO2 storage monitoring

acceptedVersio

Offset-Dependent Overburden Time-Shifts from Ultrasonic Data

Depletion or injection into a reservoir implies stress and strain changes in the reservoir and its surroundings. This may lead to measurable time-shifts for seismic waves propagating in the subsurface. We have measured multidirectional ultrasonic P-wave velocity changes for three different field shale cores, each probed with four different stress paths (i.e. different ratios between the horizontal and the vertical stress change), to systematically quantify the time-shifts for overburden shales with respect to ray angle (offset). The laboratory data show that for a given offset, the time-shifts are stress path dependent, where the isotropic stress path is associated with larger time-shifts as compared to the constant mean stress path or the triaxial stress path. Generally, the time-shifts are largest for zero offset (propagation normal to the bedding) and are decreasing for increasing offsets. The constant mean stress path has the most significant decrease of time-shifts with offset. By utilizing pre-stack seismic offset data, such controlled laboratory experiments can be used to constrain the inversion of 4D seismic data to quantify the stress and strain changes due to production. This may have important implications for improved recovery and safety, particularly in mature fields